Step 2: Prepare Datasets for Training and Testing This is the step where most of the effort is involved: the machine learning algorithms involved in NLP - and most of the state-of-the-art NLP engines out there are based on some kind of machine learning - are only as good as the data they have been trained on. It is both a question of quality as well as quantity.
Step 4: Identify NLP-related Issues and Solutions The Botium Coach Dashboard visualizes the NLP performance metrics and suggests steps for improving it. It will show any pieces of test data that either did not return the expected intent, did return the expected intent but with a low confidence score, or did return the expected intent, but with a confidence score close to another intent’s.
Step 6: Training your NLU engine For your convenience and for fast feedback cycles, Botium Box brings a Test Case Wizard for uploading training data from Botium Box to your NLU engine of choice and start the training process.